By Zareen Syed, Tim Finin (auth.), Joaquim Filipe, Ana Fred, Bernadette Sharp (eds.)
This e-book constitutes the completely refereed post-conference court cases of the second one foreign convention on brokers and synthetic Intelligence, ICAART 2010, held in Valencia, Spain, in January 2010. The 17 revised complete papers provided including an invited paper have been rigorously reviewed and chosen from 364 submissions. comparable because the convention the papers are prepared in simultaneous tracks: synthetic Intelligence and brokers. the chosen papers replicate the interdisciplinary nature of the convention. the variety of issues is a crucial function of this convention, allowing an total notion of a number of vital medical and technological trends.
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Extra info for Agents and Artificial Intelligence: Second International Conference, ICAART 2010, Valencia, Spain, January 22-24, 2010. Revised Selected Papers
Turini doc("people-d")//person-discr[not(empty(profile/interest))] let active field $business := $person/profile/business/text() let active field $education := $person/profile/education/text() let active field $has-credit-card := string(not(empty($person/creditcard))) let active field $income := $person/income-d/text() let active field $gender := $person/profile/gender/text() let active field $watch := string(count($person/watches/watch) > 5) let predicted field $has-a-music-interest := string(some $j in $person/profile/interest/@category satisfies some $k in $music-cat/@id satisfies $j eq $k) The next goal is to extract a descriptive model to find frequent correlations among the bidders in all the open auctions.
J. Filipe, A. Fred, and B. ): ICAART 2010, CCIS 129, pp. 25–38, 2011. c Springer-Verlag Berlin Heidelberg 2011 26 A. Romei and F. Turini The goal of our research is the design and implementation of a mining language in support of an IDB in which an XML native database is used as a storage for Knowledge Discovery in Databases (KDD) entities, while Data Mining (DM) tasks are expressed in an XQuery-like language, in the same way in which mining languages on relational databases are expressed in a SQL-like format.
Q4 ] With reference to the itemsets extracted in the previous query, return as output the set of triplets, (b, s, c), where b (resp. s) is the buyer (resp. seller) identifier in all the closed auctions, and c is the percentage of the itemsets that comply with b and s, over the total number of itemsets. apply itemsets doc("supp-seller-buyer") for association model $m in doc("co-bidders") let metadata field $total := xs:decimal($m/PMML//@numberOfItemsets) for data $closed in doc("xmark")/site/closed auctions/closed auction for item $sel-buy in $closed/(seller|buyer)/@person let active field $item := string($sel-buy) having supported $s, return